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Abstract Details
Activity Number:
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651
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Social Statistics Section
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Abstract - #302301 |
Title:
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Temporal Modeling Of Highway Crash Severity By Involved Person Age
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Author(s):
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Shan Hu and James Mooradian*+ and Nalini Ravishanker and John Ivan
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Companies:
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University of Connecticut and University of Connecticut and University of Connecticut
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Address:
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Department of Civil and Environmental Engineering, Storrs, CT, 06269,
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Keywords:
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Hierarchical generalized linear models ;
Predictive dynamic time series models
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Abstract:
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This project uses dynamic time series models to uncover temporal patterns in the severity level (fatal, serious injury, minor injury, slight injury and no injury) for persons involved in highway crashes in Connecticut. Existing state sources provide data describing the time and weather conditions for each crash and the vehicles and persons involved over the time period from 1995 to 2008 as well as the traffic volumes and the characteristics of the roads on which these crashes occurred. Controlling for characteristics known to be related to severity, e.g., age, gender, and road characteristics, statistical modeling enables us to predict the likelihood of an individual to have a specific severity outcome if he/she is involved in a highway crash, as well as to identify trends in the crash outcomes for senior motor vehicle travelers (65 years and over) that point to significant impacts on public health and safety as the elderly become a larger portion of the population.
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